Lam, Kin Kwok. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 99-103). / Abstracts in English and Chinese. / Abstract --- p.ii / 摘要 --- p.iii / Acknowledgements --- p.iv / Table of Contents --- p.v / List of Figures --- p.viii / List of Tables --- p.xi / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Intrinsic Problem of Today's Pose Estimation Systems --- p.1 / Chapter 1.2 --- Multi-sensors Data Fusion --- p.2 / Chapter 1.3 --- Objectives and Contributions --- p.3 / Chapter 1.4 --- Organization of the dissertation --- p.4 / Chapter Chapter 2 --- Architecture of Sensing System --- p.5 / Chapter 2.1 --- Hardware for Pose Estimation System --- p.5 / Chapter 2.2 --- Software for Pose Estimation System --- p.6 / Chapter Chapter 3 --- Inertial Measurement System --- p.7 / Chapter 3.1 --- Basic knowledge of Inertial Measurement System --- p.7 / Chapter 3.2 --- Strapdown Inertial Navigation --- p.8 / Chapter 3.2.1 --- Tracking Orientation --- p.9 / Chapter 3.2.2 --- Discussion of Attitude Representations --- p.14 / Chapter 3.2.3 --- Tracking Position --- p.16 / Chapter 3.3 --- Summary of Strapdown Inertial Navigation --- p.16 / Chapter Chapter 4 --- Visual Tracking System --- p.17 / Chapter 4.1 --- Background of Visual Tracking System --- p.17 / Chapter 4.2 --- Basic knowledge of Camera Calibration and Model --- p.18 / Chapter 4.2.1 --- Related Coordinate Frames --- p.18 / Chapter 4.2.2 --- Pinhole Camera Model --- p.20 / Chapter 4.2.3 --- Calibration for Nonlinear Model --- p.21 / Chapter 4.3 --- Implementation of Process to Calibrate Camera --- p.22 / Chapter 4.3.1 --- Image Capture and Corners Extraction --- p.22 / Chapter 4.3.2 --- Camera Calibration --- p.23 / Chapter 4.4 --- Perspective-n-Point Problem --- p.25 / Chapter 4.5 --- Camera Pose Estimation Algorithms --- p.26 / Chapter 4.5.1 --- Pose Estimation Using Quadrangular Targets --- p.27 / Chapter 4.5.2 --- Efficient Perspective-n-Point Camera Pose Estimation --- p.31 / Chapter 4.5.3 --- Linear N-Point Camera Pose Determination --- p.33 / Chapter 4.5.4 --- Pose Estimation from Orthography and Scaling with Iterations --- p.36 / Chapter 4.6 --- Experimental Results of Camera Pose Estimation Algorithms --- p.40 / Chapter 4.6.1 --- Simulation Test --- p.40 / Chapter 4.6.2 --- Real Images Test --- p.43 / Chapter 4.6.3 --- Summary --- p.46 / Chapter Chapter 5 --- Kalman Filter --- p.47 / Chapter 5.1 --- Linear Dynamic System Model --- p.48 / Chapter 5.2 --- Time Update --- p.48 / Chapter 5.3 --- Measurement Update --- p.49 / Chapter 5.3.1 --- Maximum a Posterior Probability --- p.49 / Chapter 5.3.2 --- Batch Least-Square Estimation --- p.51 / Chapter 5.3.3 --- Measurement Update in Kalman Filter --- p.54 / Chapter 5.4 --- Summary of Kalman Filter --- p.56 / Chapter Chapter 6 --- Extended Kalman Filter --- p.58 / Chapter 6.1 --- Linearization of Nonlinear Systems --- p.58 / Chapter 6.2 --- Extended Kalman Filter --- p.59 / Chapter Chapter 7 --- Unscented Kalman Filter --- p.61 / Chapter 7.1 --- Least-square Estimator Structure --- p.61 / Chapter 7.2 --- Unscented Transform --- p.62 / Chapter 7.3 --- Unscented Kalman Filter --- p.64 / Chapter Chapter 8 --- Data Fusion Algorithm --- p.68 / Chapter 8.1 --- Traditional Multi-Sensor Data Fusion --- p.69 / Chapter 8.1.1 --- Measurement Fusion --- p.69 / Chapter 8.1.2 --- Track-to-Track Fusion --- p.71 / Chapter 8.2 --- Multi-Sensor Data Fusion using Extended Kalman Filter --- p.72 / Chapter 8.2.1 --- Time Update Model --- p.73 / Chapter 8.2.2 --- Measurement Update Model --- p.74 / Chapter 8.3 --- Multi-Sensor Data Fusion using Unscented Kalman Filter --- p.75 / Chapter 8.3.1 --- Time Update Model --- p.75 / Chapter 8.3.2 --- Measurement Update Model --- p.76 / Chapter 8.4 --- Simulation Test --- p.76 / Chapter 8.5 --- Experimental Test --- p.80 / Chapter 8.5.1 --- Rotational Test --- p.81 / Chapter 8.5.2 --- Translational Test --- p.86 / Chapter Chapter 9 --- Future Work --- p.93 / Chapter 9.1 --- Zero Velocity Compensation --- p.93 / Chapter 9.1.1 --- Stroke Segmentation --- p.93 / Chapter 9.1.2 --- Zero Velocity Compensation (ZVC) --- p.94 / Chapter 9.1.3 --- Experimental Results --- p.94 / Chapter 9.2 --- Random Sample Consensus Algorithm (RANSAC) --- p.96 / Chapter Chapter 10 --- Conclusion --- p.97 / Bibliography --- p.99
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_327333 |
| Date | January 2011 |
| Contributors | Lam, Kin Kwok., Chinese University of Hong Kong Graduate School. Division of Mechanical and Automation Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xi, 103 leaves : ill. (some col.) ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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